Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations750000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory103.0 MiB
Average record size in memory144.0 B

Variable types

Numeric8
Categorical7
Boolean3

Alerts

contact is highly overall correlated with monthHigh correlation
education is highly overall correlated with jobHigh correlation
housing is highly overall correlated with monthHigh correlation
job is highly overall correlated with educationHigh correlation
month is highly overall correlated with contact and 1 other fieldsHigh correlation
pdays is highly overall correlated with poutcome and 1 other fieldsHigh correlation
poutcome is highly overall correlated with pdaysHigh correlation
previous is highly overall correlated with pdaysHigh correlation
default is highly imbalanced (87.5%) Imbalance
poutcome is highly imbalanced (68.8%) Imbalance
id is uniformly distributed Uniform
id has unique values Unique
balance has 93159 (12.4%) zeros Zeros
previous has 672431 (89.7%) zeros Zeros

Reproduction

Analysis started2025-08-09 03:38:28.192554
Analysis finished2025-08-09 03:39:07.591382
Duration39.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct750000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374999.5
Minimum0
Maximum749999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:07.731678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37499.95
Q1187499.75
median374999.5
Q3562499.25
95-th percentile712499.05
Maximum749999
Range749999
Interquartile range (IQR)374999.5

Descriptive statistics

Standard deviation216506.5
Coefficient of variation (CV)0.57735142
Kurtosis-1.2
Mean374999.5
Median Absolute Deviation (MAD)187500
Skewness-1.9631002 × 10-15
Sum2.8124962 × 1011
Variance4.6875062 × 1010
MonotonicityStrictly increasing
2025-08-09T09:39:07.883147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
749983 1
 
< 0.1%
749982 1
 
< 0.1%
749981 1
 
< 0.1%
749980 1
 
< 0.1%
749979 1
 
< 0.1%
749978 1
 
< 0.1%
749977 1
 
< 0.1%
749976 1
 
< 0.1%
749975 1
 
< 0.1%
749974 1
 
< 0.1%
Other values (749990) 749990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
749999 1
< 0.1%
749998 1
< 0.1%
749997 1
< 0.1%
749996 1
< 0.1%
749995 1
< 0.1%
749994 1
< 0.1%
749993 1
< 0.1%
749992 1
< 0.1%
749991 1
< 0.1%
749990 1
< 0.1%

age
Real number (ℝ)

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.926395
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:08.031257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q133
median39
Q348
95-th percentile58
Maximum95
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.098829
Coefficient of variation (CV)0.24675589
Kurtosis-0.06956087
Mean40.926395
Median Absolute Deviation (MAD)7
Skewness0.58613732
Sum30694796
Variance101.98634
MonotonicityNot monotonic
2025-08-09T09:39:08.170823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 37235
 
5.0%
33 34511
 
4.6%
31 34301
 
4.6%
34 34131
 
4.6%
35 33057
 
4.4%
36 31022
 
4.1%
30 29350
 
3.9%
37 27988
 
3.7%
39 25380
 
3.4%
38 24429
 
3.3%
Other values (68) 438596
58.5%
ValueCountFrequency (%)
18 80
 
< 0.1%
19 258
 
< 0.1%
20 392
 
0.1%
21 788
 
0.1%
22 1478
 
0.2%
23 2476
 
0.3%
24 4162
 
0.6%
25 7226
1.0%
26 11779
1.6%
27 13207
1.8%
ValueCountFrequency (%)
95 3
 
< 0.1%
94 4
 
< 0.1%
93 12
 
< 0.1%
92 5
 
< 0.1%
91 1
 
< 0.1%
90 6
 
< 0.1%
89 10
 
< 0.1%
88 6
 
< 0.1%
87 26
 
< 0.1%
86 65
< 0.1%

job
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
management
175541 
blue-collar
170498 
technician
138107 
admin.
81492 
services
64209 
Other values (7)
120153 

Length

Max length13
Median length12
Mean length9.5241187
Min length6

Characters and Unicode

Total characters7143089
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtechnician
2nd rowblue-collar
3rd rowblue-collar
4th rowstudent
5th rowtechnician

Common Values

ValueCountFrequency (%)
management 175541
23.4%
blue-collar 170498
22.7%
technician 138107
18.4%
admin. 81492
10.9%
services 64209
 
8.6%
retired 35185
 
4.7%
self-employed 19020
 
2.5%
entrepreneur 17718
 
2.4%
unemployed 17634
 
2.4%
housemaid 15912
 
2.1%
Other values (2) 14684
 
2.0%

Length

2025-08-09T09:39:08.463330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
management 175541
23.4%
blue-collar 170498
22.7%
technician 138107
18.4%
admin 81492
10.9%
services 64209
 
8.6%
retired 35185
 
4.7%
self-employed 19020
 
2.5%
entrepreneur 17718
 
2.4%
unemployed 17634
 
2.4%
housemaid 15912
 
2.1%
Other values (2) 14684
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 1049354
14.7%
n 782376
11.0%
a 757091
10.6%
l 567168
 
7.9%
c 510921
 
7.2%
m 485140
 
6.8%
i 473012
 
6.6%
t 390085
 
5.5%
r 358231
 
5.0%
u 236446
 
3.3%
Other values (14) 1533265
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7143089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1049354
14.7%
n 782376
11.0%
a 757091
10.6%
l 567168
 
7.9%
c 510921
 
7.2%
m 485140
 
6.8%
i 473012
 
6.6%
t 390085
 
5.5%
r 358231
 
5.0%
u 236446
 
3.3%
Other values (14) 1533265
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7143089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1049354
14.7%
n 782376
11.0%
a 757091
10.6%
l 567168
 
7.9%
c 510921
 
7.2%
m 485140
 
6.8%
i 473012
 
6.6%
t 390085
 
5.5%
r 358231
 
5.0%
u 236446
 
3.3%
Other values (14) 1533265
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7143089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1049354
14.7%
n 782376
11.0%
a 757091
10.6%
l 567168
 
7.9%
c 510921
 
7.2%
m 485140
 
6.8%
i 473012
 
6.6%
t 390085
 
5.5%
r 358231
 
5.0%
u 236446
 
3.3%
Other values (14) 1533265
21.5%

marital
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
married
480759 
single
194834 
divorced
74407 

Length

Max length8
Median length7
Mean length6.8394307
Min length6

Characters and Unicode

Total characters5129573
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowsingle
5th rowmarried

Common Values

ValueCountFrequency (%)
married 480759
64.1%
single 194834
26.0%
divorced 74407
 
9.9%

Length

2025-08-09T09:39:08.587529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T09:39:08.685375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 480759
64.1%
single 194834
26.0%
divorced 74407
 
9.9%

Most occurring characters

ValueCountFrequency (%)
r 1035925
20.2%
e 750000
14.6%
i 750000
14.6%
d 629573
12.3%
m 480759
9.4%
a 480759
9.4%
s 194834
 
3.8%
n 194834
 
3.8%
g 194834
 
3.8%
l 194834
 
3.8%
Other values (3) 223221
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5129573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1035925
20.2%
e 750000
14.6%
i 750000
14.6%
d 629573
12.3%
m 480759
9.4%
a 480759
9.4%
s 194834
 
3.8%
n 194834
 
3.8%
g 194834
 
3.8%
l 194834
 
3.8%
Other values (3) 223221
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5129573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1035925
20.2%
e 750000
14.6%
i 750000
14.6%
d 629573
12.3%
m 480759
9.4%
a 480759
9.4%
s 194834
 
3.8%
n 194834
 
3.8%
g 194834
 
3.8%
l 194834
 
3.8%
Other values (3) 223221
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5129573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1035925
20.2%
e 750000
14.6%
i 750000
14.6%
d 629573
12.3%
m 480759
9.4%
a 480759
9.4%
s 194834
 
3.8%
n 194834
 
3.8%
g 194834
 
3.8%
l 194834
 
3.8%
Other values (3) 223221
 
4.4%

education
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
secondary
401683 
tertiary
227508 
primary
99510 
unknown
 
21299

Length

Max length9
Median length9
Mean length8.3744987
Min length7

Characters and Unicode

Total characters6280874
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsecondary
2nd rowsecondary
3rd rowsecondary
4th rowsecondary
5th rowsecondary

Common Values

ValueCountFrequency (%)
secondary 401683
53.6%
tertiary 227508
30.3%
primary 99510
 
13.3%
unknown 21299
 
2.8%

Length

2025-08-09T09:39:08.816905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T09:39:08.899219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
secondary 401683
53.6%
tertiary 227508
30.3%
primary 99510
 
13.3%
unknown 21299
 
2.8%

Most occurring characters

ValueCountFrequency (%)
r 1055719
16.8%
a 728701
11.6%
y 728701
11.6%
e 629191
10.0%
n 465580
7.4%
t 455016
7.2%
o 422982
6.7%
s 401683
 
6.4%
d 401683
 
6.4%
c 401683
 
6.4%
Other values (6) 589935
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6280874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1055719
16.8%
a 728701
11.6%
y 728701
11.6%
e 629191
10.0%
n 465580
7.4%
t 455016
7.2%
o 422982
6.7%
s 401683
 
6.4%
d 401683
 
6.4%
c 401683
 
6.4%
Other values (6) 589935
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6280874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1055719
16.8%
a 728701
11.6%
y 728701
11.6%
e 629191
10.0%
n 465580
7.4%
t 455016
7.2%
o 422982
6.7%
s 401683
 
6.4%
d 401683
 
6.4%
c 401683
 
6.4%
Other values (6) 589935
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6280874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1055719
16.8%
a 728701
11.6%
y 728701
11.6%
e 629191
10.0%
n 465580
7.4%
t 455016
7.2%
o 422982
6.7%
s 401683
 
6.4%
d 401683
 
6.4%
c 401683
 
6.4%
Other values (6) 589935
9.4%

default
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.5 KiB
False
737151 
True
 
12849
ValueCountFrequency (%)
False 737151
98.3%
True 12849
 
1.7%
2025-08-09T09:39:08.965001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

balance
Real number (ℝ)

Zeros 

Distinct8217
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1204.0674
Minimum-8019
Maximum99717
Zeros93159
Zeros (%)12.4%
Negative104645
Negative (%)14.0%
Memory size5.7 MiB
2025-08-09T09:39:09.066263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8019
5-th percentile-287
Q10
median634
Q31390
95-th percentile4493
Maximum99717
Range107736
Interquartile range (IQR)1390

Descriptive statistics

Standard deviation2836.0968
Coefficient of variation (CV)2.3554302
Kurtosis268.86362
Mean1204.0674
Median Absolute Deviation (MAD)634
Skewness12.304123
Sum9.0305055 × 108
Variance8043444.8
MonotonicityNot monotonic
2025-08-09T09:39:09.199759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93159
 
12.4%
5 4174
 
0.6%
4 3741
 
0.5%
-1 3573
 
0.5%
3 3429
 
0.5%
6 2987
 
0.4%
2 2803
 
0.4%
8 2696
 
0.4%
1 2439
 
0.3%
7 2070
 
0.3%
Other values (8207) 628929
83.9%
ValueCountFrequency (%)
-8019 75
< 0.1%
-8016 1
 
< 0.1%
-7048 1
 
< 0.1%
-6857 2
 
< 0.1%
-6848 1
 
< 0.1%
-6847 27
 
< 0.1%
-6815 1
 
< 0.1%
-4057 5
 
< 0.1%
-3748 1
 
< 0.1%
-3542 1
 
< 0.1%
ValueCountFrequency (%)
99717 1
 
< 0.1%
99218 1
 
< 0.1%
98942 1
 
< 0.1%
98418 2
 
< 0.1%
98417 37
< 0.1%
98410 1
 
< 0.1%
98217 1
 
< 0.1%
96110 1
 
< 0.1%
88988 1
 
< 0.1%
88904 1
 
< 0.1%

housing
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.5 KiB
True
411288 
False
338712 
ValueCountFrequency (%)
True 411288
54.8%
False 338712
45.2%
2025-08-09T09:39:09.281678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.5 KiB
False
645023 
True
104977 
ValueCountFrequency (%)
False 645023
86.0%
True 104977
 
14.0%
2025-08-09T09:39:09.324061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

contact
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
cellular
486655 
unknown
231627 
telephone
 
31718

Length

Max length9
Median length8
Mean length7.7334547
Min length7

Characters and Unicode

Total characters5800091
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 486655
64.9%
unknown 231627
30.9%
telephone 31718
 
4.2%

Length

2025-08-09T09:39:09.419159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T09:39:09.501251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cellular 486655
64.9%
unknown 231627
30.9%
telephone 31718
 
4.2%

Most occurring characters

ValueCountFrequency (%)
l 1491683
25.7%
n 726599
12.5%
u 718282
12.4%
e 581809
 
10.0%
c 486655
 
8.4%
a 486655
 
8.4%
r 486655
 
8.4%
o 263345
 
4.5%
k 231627
 
4.0%
w 231627
 
4.0%
Other values (3) 95154
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5800091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1491683
25.7%
n 726599
12.5%
u 718282
12.4%
e 581809
 
10.0%
c 486655
 
8.4%
a 486655
 
8.4%
r 486655
 
8.4%
o 263345
 
4.5%
k 231627
 
4.0%
w 231627
 
4.0%
Other values (3) 95154
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5800091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1491683
25.7%
n 726599
12.5%
u 718282
12.4%
e 581809
 
10.0%
c 486655
 
8.4%
a 486655
 
8.4%
r 486655
 
8.4%
o 263345
 
4.5%
k 231627
 
4.0%
w 231627
 
4.0%
Other values (3) 95154
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5800091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1491683
25.7%
n 726599
12.5%
u 718282
12.4%
e 581809
 
10.0%
c 486655
 
8.4%
a 486655
 
8.4%
r 486655
 
8.4%
o 263345
 
4.5%
k 231627
 
4.0%
w 231627
 
4.0%
Other values (3) 95154
 
1.6%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.117209
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:09.581509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median17
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.2508323
Coefficient of variation (CV)0.51192686
Kurtosis-1.0444584
Mean16.117209
Median Absolute Deviation (MAD)6
Skewness0.054014179
Sum12087907
Variance68.076234
MonotonicityNot monotonic
2025-08-09T09:39:09.706838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 51431
 
6.9%
18 41352
 
5.5%
21 36395
 
4.9%
28 33479
 
4.5%
17 33428
 
4.5%
14 32854
 
4.4%
8 30996
 
4.1%
6 30573
 
4.1%
5 30245
 
4.0%
19 29948
 
4.0%
Other values (21) 399299
53.2%
ValueCountFrequency (%)
1 3890
 
0.5%
2 20003
2.7%
3 15827
2.1%
4 22270
3.0%
5 30245
4.0%
6 30573
4.1%
7 28771
3.8%
8 30996
4.1%
9 24752
3.3%
10 7626
 
1.0%
ValueCountFrequency (%)
31 11159
 
1.5%
30 26181
3.5%
29 29858
4.0%
28 33479
4.5%
27 19586
2.6%
26 16216
2.2%
25 13492
1.8%
24 6241
 
0.8%
23 14539
1.9%
22 15128
2.0%

month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
may
228411 
aug
128859 
jul
110647 
jun
93670 
nov
66062 
Other values (7)
122351 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2250000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaug
2nd rowjun
3rd rowmay
4th rowmay
5th rowfeb

Common Values

ValueCountFrequency (%)
may 228411
30.5%
aug 128859
17.2%
jul 110647
14.8%
jun 93670
12.5%
nov 66062
 
8.8%
apr 41319
 
5.5%
feb 37611
 
5.0%
jan 18937
 
2.5%
oct 9204
 
1.2%
sep 7409
 
1.0%
Other values (2) 7871
 
1.0%

Length

2025-08-09T09:39:09.818304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 228411
30.5%
aug 128859
17.2%
jul 110647
14.8%
jun 93670
12.5%
nov 66062
 
8.8%
apr 41319
 
5.5%
feb 37611
 
5.0%
jan 18937
 
2.5%
oct 9204
 
1.2%
sep 7409
 
1.0%
Other values (2) 7871
 
1.0%

Most occurring characters

ValueCountFrequency (%)
a 423328
18.8%
u 333176
14.8%
m 234213
10.4%
y 228411
10.2%
j 223254
9.9%
n 178669
7.9%
g 128859
 
5.7%
l 110647
 
4.9%
o 75266
 
3.3%
v 66062
 
2.9%
Other values (9) 248115
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2250000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 423328
18.8%
u 333176
14.8%
m 234213
10.4%
y 228411
10.2%
j 223254
9.9%
n 178669
7.9%
g 128859
 
5.7%
l 110647
 
4.9%
o 75266
 
3.3%
v 66062
 
2.9%
Other values (9) 248115
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2250000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 423328
18.8%
u 333176
14.8%
m 234213
10.4%
y 228411
10.2%
j 223254
9.9%
n 178669
7.9%
g 128859
 
5.7%
l 110647
 
4.9%
o 75266
 
3.3%
v 66062
 
2.9%
Other values (9) 248115
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2250000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 423328
18.8%
u 333176
14.8%
m 234213
10.4%
y 228411
10.2%
j 223254
9.9%
n 178669
7.9%
g 128859
 
5.7%
l 110647
 
4.9%
o 75266
 
3.3%
v 66062
 
2.9%
Other values (9) 248115
11.0%

duration
Real number (ℝ)

Distinct1760
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.22914
Minimum1
Maximum4918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:09.933261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q191
median133
Q3361
95-th percentile829
Maximum4918
Range4917
Interquartile range (IQR)270

Descriptive statistics

Standard deviation272.55566
Coefficient of variation (CV)1.0637184
Kurtosis6.4345227
Mean256.22914
Median Absolute Deviation (MAD)57
Skewness2.0487765
Sum1.9217186 × 108
Variance74286.589
MonotonicityNot monotonic
2025-08-09T09:39:10.073416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 8599
 
1.1%
114 7485
 
1.0%
121 7307
 
1.0%
112 7250
 
1.0%
111 6834
 
0.9%
73 6749
 
0.9%
90 6677
 
0.9%
103 6646
 
0.9%
123 6585
 
0.9%
88 6334
 
0.8%
Other values (1750) 679534
90.6%
ValueCountFrequency (%)
1 6
 
< 0.1%
3 1
 
< 0.1%
4 465
 
0.1%
5 2733
0.4%
6 3592
0.5%
7 5554
0.7%
8 4924
0.7%
9 3181
0.4%
10 2536
0.3%
11 1907
 
0.3%
ValueCountFrequency (%)
4918 1
< 0.1%
4916 2
< 0.1%
4914 2
< 0.1%
4912 1
< 0.1%
4903 1
< 0.1%
4860 1
< 0.1%
4854 1
< 0.1%
4825 1
< 0.1%
4824 1
< 0.1%
4823 1
< 0.1%

campaign
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.577008
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:10.198510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum63
Range62
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7185137
Coefficient of variation (CV)1.0549109
Kurtosis37.493557
Mean2.577008
Median Absolute Deviation (MAD)1
Skewness4.8104367
Sum1932756
Variance7.390317
MonotonicityNot monotonic
2025-08-09T09:39:10.336256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 304481
40.6%
2 209834
28.0%
3 88683
 
11.8%
4 60974
 
8.1%
5 25648
 
3.4%
6 19694
 
2.6%
7 10360
 
1.4%
8 7777
 
1.0%
9 3993
 
0.5%
10 3511
 
0.5%
Other values (42) 15045
 
2.0%
ValueCountFrequency (%)
1 304481
40.6%
2 209834
28.0%
3 88683
 
11.8%
4 60974
 
8.1%
5 25648
 
3.4%
6 19694
 
2.6%
7 10360
 
1.4%
8 7777
 
1.0%
9 3993
 
0.5%
10 3511
 
0.5%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
51 7
< 0.1%
50 11
< 0.1%
48 2
 
< 0.1%
46 8
< 0.1%
45 4
 
< 0.1%
44 10
< 0.1%
43 16
< 0.1%

pdays
Real number (ℝ)

High correlation 

Distinct596
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.412733
Minimum-1
Maximum871
Zeros1
Zeros (%)< 0.1%
Negative672434
Negative (%)89.7%
Memory size5.7 MiB
2025-08-09T09:39:10.475109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile189
Maximum871
Range872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation77.319998
Coefficient of variation (CV)3.4498246
Kurtosis13.597849
Mean22.412733
Median Absolute Deviation (MAD)0
Skewness3.6250487
Sum16809550
Variance5978.3821
MonotonicityNot monotonic
2025-08-09T09:39:10.614446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 672434
89.7%
182 2515
 
0.3%
92 2275
 
0.3%
183 2074
 
0.3%
181 1698
 
0.2%
91 1675
 
0.2%
370 1532
 
0.2%
184 1293
 
0.2%
350 1184
 
0.2%
364 942
 
0.1%
Other values (586) 62378
 
8.3%
ValueCountFrequency (%)
-1 672434
89.7%
0 1
 
< 0.1%
1 45
 
< 0.1%
2 210
 
< 0.1%
3 1
 
< 0.1%
4 8
 
< 0.1%
5 72
 
< 0.1%
6 46
 
< 0.1%
7 18
 
< 0.1%
8 165
 
< 0.1%
ValueCountFrequency (%)
871 2
 
< 0.1%
854 2
 
< 0.1%
850 1
 
< 0.1%
842 5
< 0.1%
838 4
< 0.1%
835 1
 
< 0.1%
831 4
< 0.1%
828 3
< 0.1%
826 5
< 0.1%
825 1
 
< 0.1%

previous
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29854533
Minimum0
Maximum200
Zeros672431
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-09T09:39:10.756386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum200
Range200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.335926
Coefficient of variation (CV)4.4747845
Kurtosis820.30476
Mean0.29854533
Median Absolute Deviation (MAD)0
Skewness13.749885
Sum223909
Variance1.7846984
MonotonicityNot monotonic
2025-08-09T09:39:10.899644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 672431
89.7%
1 28342
 
3.8%
2 20468
 
2.7%
3 10326
 
1.4%
4 6239
 
0.8%
5 3882
 
0.5%
6 2183
 
0.3%
7 1730
 
0.2%
8 1036
 
0.1%
9 697
 
0.1%
Other values (40) 2666
 
0.4%
ValueCountFrequency (%)
0 672431
89.7%
1 28342
 
3.8%
2 20468
 
2.7%
3 10326
 
1.4%
4 6239
 
0.8%
5 3882
 
0.5%
6 2183
 
0.3%
7 1730
 
0.2%
8 1036
 
0.1%
9 697
 
0.1%
ValueCountFrequency (%)
200 1
 
< 0.1%
58 1
 
< 0.1%
55 2
< 0.1%
51 4
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
43 4
< 0.1%
41 4
< 0.1%
40 1
 
< 0.1%

poutcome
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
unknown
672450 
failure
 
45115
success
 
17691
other
 
14744

Length

Max length7
Median length7
Mean length6.9606827
Min length5

Characters and Unicode

Total characters5220512
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 672450
89.7%
failure 45115
 
6.0%
success 17691
 
2.4%
other 14744
 
2.0%

Length

2025-08-09T09:39:11.195999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T09:39:11.278778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 672450
89.7%
failure 45115
 
6.0%
success 17691
 
2.4%
other 14744
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n 2017350
38.6%
u 735256
 
14.1%
o 687194
 
13.2%
k 672450
 
12.9%
w 672450
 
12.9%
e 77550
 
1.5%
r 59859
 
1.1%
s 53073
 
1.0%
f 45115
 
0.9%
a 45115
 
0.9%
Other values (5) 155100
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5220512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2017350
38.6%
u 735256
 
14.1%
o 687194
 
13.2%
k 672450
 
12.9%
w 672450
 
12.9%
e 77550
 
1.5%
r 59859
 
1.1%
s 53073
 
1.0%
f 45115
 
0.9%
a 45115
 
0.9%
Other values (5) 155100
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5220512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2017350
38.6%
u 735256
 
14.1%
o 687194
 
13.2%
k 672450
 
12.9%
w 672450
 
12.9%
e 77550
 
1.5%
r 59859
 
1.1%
s 53073
 
1.0%
f 45115
 
0.9%
a 45115
 
0.9%
Other values (5) 155100
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5220512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2017350
38.6%
u 735256
 
14.1%
o 687194
 
13.2%
k 672450
 
12.9%
w 672450
 
12.9%
e 77550
 
1.5%
r 59859
 
1.1%
s 53073
 
1.0%
f 45115
 
0.9%
a 45115
 
0.9%
Other values (5) 155100
 
3.0%

y
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
0
659512 
1
90488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Length

2025-08-09T09:39:11.369548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T09:39:11.441772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 659512
87.9%
1 90488
 
12.1%

Interactions

2025-08-09T09:39:03.573063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:52.494351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:54.186417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:55.770844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:57.292835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:58.819567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:00.327944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:02.093304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:03.771658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:52.702651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:54.384830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:55.966088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:57.485633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.007005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:00.550771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:02.298654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:03.968786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:52.908542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:54.572857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:56.149269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:57.672083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.195330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:00.751450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:02.491440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:04.160091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:53.107671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:54.770090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:56.328960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:57.861669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.376879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:00.952101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:02.672412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:04.341061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:53.293236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:54.966041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:56.537476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:58.042077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.572455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:01.304415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:02.849194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:04.528296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:53.488685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:55.166589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:56.739501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:58.239059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.752178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:01.505606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:03.035409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:04.724658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:53.683423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:55.357561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:56.921566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:58.440226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:59.934008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:01.701446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:03.210308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:04.914413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:53.868487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:55.576844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:57.102605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:38:58.621566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:00.123150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:01.891255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-09T09:39:03.378741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-09T09:39:11.531539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agebalancecampaigncontactdaydefaultdurationeducationhousingidjobloanmaritalmonthpdayspoutcomepreviousy
age1.0000.0630.0310.148-0.0170.022-0.0480.1360.218-0.0000.2400.0500.3470.097-0.0160.071-0.0130.169
balance0.0631.000-0.0500.043-0.0270.0460.1690.0520.0640.0020.0350.0770.0150.0580.0540.0360.0610.130
campaign0.031-0.0501.0000.0310.1690.024-0.1520.0140.028-0.0000.0130.0210.0070.051-0.0870.030-0.0860.044
contact0.1480.0430.0311.0000.1030.0200.0410.1390.2590.0020.1750.0090.0490.5430.1560.1580.0100.160
day-0.017-0.0270.1690.1031.0000.026-0.1070.0470.120-0.0020.0490.0610.0490.288-0.0840.077-0.0820.096
default0.0220.0460.0240.0200.0261.0000.0350.0160.0100.0000.0330.0900.0150.0550.0250.0250.0000.030
duration-0.0480.169-0.1520.041-0.1070.0351.0000.0140.0250.0020.0200.0300.0300.0310.0770.0430.0790.467
education0.1360.0520.0140.1390.0470.0160.0141.0000.1440.0020.5050.0790.1280.1320.0450.0310.0020.089
housing0.2180.0640.0280.2590.1200.0100.0250.1441.0000.0000.3010.0640.0300.5690.1560.1470.0070.154
id-0.0000.002-0.0000.002-0.0020.0000.0020.0020.0001.0000.0010.0000.0000.001-0.0000.000-0.0000.000
job0.2400.0350.0130.1750.0490.0330.0200.5050.3010.0011.0000.1040.2090.1260.0420.0620.0000.157
loan0.0500.0770.0210.0090.0610.0900.0300.0790.0640.0000.1041.0000.0510.1970.0260.0480.0000.082
marital0.3470.0150.0070.0490.0490.0150.0300.1280.0300.0000.2090.0511.0000.0850.0270.0330.0060.091
month0.0970.0580.0510.5430.2880.0550.0310.1320.5690.0010.1260.1970.0851.0000.1490.1920.0130.264
pdays-0.0160.054-0.0870.156-0.0840.0250.0770.0450.156-0.0000.0420.0260.0270.1491.0000.6010.9950.215
poutcome0.0710.0360.0300.1580.0770.0250.0430.0310.1470.0000.0620.0480.0330.1920.6011.0000.0500.309
previous-0.0130.061-0.0860.010-0.0820.0000.0790.0020.007-0.0000.0000.0000.0060.0130.9950.0501.0000.004
y0.1690.1300.0440.1600.0960.0300.4670.0890.1540.0000.1570.0820.0910.2640.2150.3090.0041.000

Missing values

2025-08-09T09:39:05.310217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-09T09:39:06.127216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idagejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
0042technicianmarriedsecondaryno7nonocellular25aug1173-10unknown0
1138blue-collarmarriedsecondaryno514nonounknown18jun1851-10unknown0
2236blue-collarmarriedsecondaryno602yesnounknown14may1112-10unknown0
3327studentsinglesecondaryno34yesnounknown28may102-10unknown0
4426technicianmarriedsecondaryno889yesnocellular3feb9021-10unknown1
5524admin.singlesecondaryno1882yesnocellular20apr10103-10unknown0
6639blue-collarmarriedsecondaryno0nonotelephone21nov901-10unknown0
7750admin.singlesecondaryno1595nonotelephone31jul4925-10unknown0
8846blue-collarmarriedprimaryno1463nonocellular4aug501-10unknown0
9939managementdivorcedtertiaryno25yesnocellular8may1191-10unknown0
idagejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
74999074999034blue-collarmarriedsecondaryno1385nonocellular26apr1342-10unknown0
74999174999152managementmarriedtertiaryno1359nonocellular19aug1245-10unknown0
74999274999256servicesmarriedsecondaryno2089yesnocellular20apr7833051failure0
74999374999341managementmarriedsecondaryyes-1300yesyescellular17nov1291-10unknown0
74999474999431housemaidsinglesecondaryno594nonocellular4feb1371-10unknown0
74999574999529servicessinglesecondaryno1282noyesunknown4jul10062-10unknown1
74999674999669retireddivorcedtertiaryno631nonocellular19aug871-10unknown0
74999774999750blue-collarmarriedsecondaryno217yesnocellular17apr1131-10unknown0
74999874999832technicianmarriedsecondaryno-274nonocellular26aug1086-10unknown0
74999974999942technicianmarriedsecondaryno1559nonocellular4aug143117failure0